News

Tutorial on Dynamic Models for Intensive Longitudinal Data (Faleh, Morelli et al.) accepted

Wed, 06 May 2026 18:37:30 +0200

The tutorial on DLC-SEM to model intensive longitudinal data with heterogeneous development trajectories has been accepted for publication. Starting with Bayesian CFA and time series implementations, we build complex dynamic structural equation models (DSEM) and its extension with Hidden Markov Models to model sudden changes and factorial switches. This hands-on tutorial is aimed at applied users as well as students who want to learn about this framework.

Faleh, R., Sofia, M., Andriamiarana, V., Roman, Z. J., Flückiger, C., & Brandt, H. (accepted for publication). Dynamic Latent Class Structural Equation Modeling: A Hands-On Tutorial for Modeling Intensive Longitudinal Data. Psychological Methods. Article Github Repository

New Publication: Common outcomes: conceptual and analytic issues. Quality & Quantity. Glaesser, J.

Mon, 04 May 2026 09:32:24 +0200

The challenges of studying rare events such as diseases affecting just a minority of people have received some attention in the literature. By contrast, the opposite of rare events, i.e. common events or experiences, have received less attention, perhaps because it is harder to develop a theoretical framework for such events. Nevertheless, some research questions of interest do concern common events and therefore researchers ought to pay attention to analytic challenges arising from skewed outcome distributions and asymmetry. This paper draws on Qualitative Comparative Analysis (QCA), a method well suited to analysing asymmetry, new goodness-of-fit indicators developed by Luna De Souter, and classification indices. The conventional consistency index used in QCA to assess how close a condition is to being sufficient for some outcome is of limited use for causal analysis if the outcome is very common because if an outcome is common, then it will tend also to be common for cases characterised by a wide range of conditions, and consistency for these conditions will be high accordingly, whether or not a causal link exists between them and the outcome. Using numerical examples and an empirical illustration drawing on the German ALLBUS data, the paper demonstrates that especially in the case of skewed outcome distributions additional indicators such as those developed by De Souter in conjunction with theoretical considerations will be helpful for the interpretation of findings. The paper focuses on high prevalence, but De Souter’s indicators would also be instructive for the other form of skewness, rare outcomes.

Glaesser, J. (2026). Common outcomes: conceptual and analytic issues. Quality & Quantity. https://doi.org/10.1007/s11135-025-02508-w

5th Fall School of the Methods Center

Thu, 26 Feb 2026 10:26:06 +0100

The Methods Center at the University of Tübingen invites PhD candidates, postdoctoral researchers, and professors to its 5th interdisciplinary Fall School. This year’s program focuses on advanced AI methods for research design and analysis.

The two-day event features three workshops led by PD Dr. Rudolf Debelak (University of Zürich & EPFL), Prof. Dr. Fritz Günther (Humboldt University Berlin), and Prof. Dr. Martin Spindler (University of Hamburg). Topics include automated item generation with agentic AI, the use of large language models as research assistants, and applied causal inference with AI.

Join us for an intensive exchange on innovative AI-driven methods in empirical research.

New article on frequentist forecasting in intensive longitudinal data

Thu, 22 Jan 2026 10:53:15 +0100

We introduce a first frequentist filter for regime-switching state-space (RSSS) models which allows hidden Markov(-switching) models to depend on both latent within- and between-individual characteristics. In an empirical study, the filter is applied to forecast emotions and behavior related to student dropout in math. Parameter recovery and prediction of regime and dynamic latent variables are evaluated through simulation study.

Okuyama, K., Schaffland, T. F., Kilian, P., Brandt, H., & Kelava, A. (2025). Frequentist forecasting in regime-switching models with extended Hamilton filter. PsyArXiv preprint. https://doi.org/10.48550/arXiv.2512.18149 (manuscript under review)

New paper on transfer learning of individual treatment effects

Tue, 13 Jan 2026 13:00:46 +0100

We submitted a paper showing that individual treatment effect estimation from the TARNet model can be improved in small or systematically different target datasets by transferring causal knowledge from larger source data. Across simulations and empirical data, we show that transfer learning reduces ITE estimation error and attenuates bias particularly when a large, unbiased source dataset is available and the target sample is small or potentially biased.

Aydin, S. B., & Brandt, H. (submitted). Advantages and limitations in the use of transfer learning for individual treatment effects in causal machine learning. Article Github

Prof. Dr. Ursula Offenberger erhält zwei Lehrpreise

Fri, 31 Oct 2025 09:38:00 +0100

Im Oktober 2025 erhielt das aus einem Masterseminar heraus entstandene Theaterprojekt “Drawing Lines. Vom Kampf um gleiche Rechte” den Lehrpreis der Universität Tübingen. In einem Festakt am 4. Dezember 2025 erfolgte außerdem die Auszeichnung mit dem Landeslehrpreis durch die baden-württembergische Wissenschaftsministerin Petra Olschowski.

Fall School October 6 and 7, 2025

Wed, 01 Oct 2025 09:33:32 +0200

We will hold our 4th interdisciplinary Fall School this upcoming Monday and Tuesday. We are proud that Prof. Marsman and Prof. Vansteelandt will hold two workshops on network analysis and causal machine learning.

All details can be found here

Prof. Brandt ends his term as head of the German Methods Group

Wed, 01 Oct 2025 09:08:15 +0200

Following the recent election, Prof. Brandt stepped down and officially transferred leadership of the Methods Group (Fachgruppe Methoden und Evaluation der Deutschen Gesellschaft für Psychologie) to Prof. Andreas Frey (University of Frankfurt) at the general assembly of the group on Monday, 29 September. Prof. Brandt was head of the group since 2021 and was particularly involved in improving conditions for young scientists, the development of open science practices and future integration of quantitative methods and psychometrics in the changing environment of Psychology at German universities.

Berg, M., Suchotzki, K., Zimmermann, J., Merz, C. J., Szota, K., Brandt, H., Hartwigsen, G., Lincoln, T. M., Mokros, A., Gade, M., Niessen, C., Rauthmann, J., Hoehl, S., Kubiak, T., Franke, T., Frischlich, L., Degner, J., Matthies, E., Sparfeldt, J. R., Rief, W., Haberkamp, A., & the PsyChange Network (2025). A Hitchhiker’s Guide to Translation – Ideas for Fostering and Disseminating (Clinical) Translational Psychology, European Psychologist. doi: 10.1027/1016-9040/a000555.

Brandt, H., Henninger, M., Ulitzsch, E., Kleinke, K., & Schäfer, T. (2023). Responsible research assessment in the area of methodological or quantitative research: A comment on Gärtner et al. (2022). Meta-Psychology, 7, MP.2023.3796. doi: 10.15626/MP.2023.3796

Symposium Improving Dynamic Modeling of Intensive Longitudinal Data at FGME conference

Wed, 01 Oct 2025 09:01:25 +0200

We presented a symposium at the 17th Conference of the Methods & Evaluation Section of the German Psychological Society (DGPs) in Berlin. Our workgroup included 4 talks on challenges in (intensive) longitudinal data: heterogeneity across/within subjects, complex temporal dynamics, unmeasured confounding, and model uncertainty—that require specialized estimation, diagnostics, and scalable algorithms.

Details on the symposium can be found here:

New submission for causal inference in SEM under confounding (Morelli et al., submitted)

Wed, 01 Oct 2025 08:59:38 +0200

We just submitted a new paper on latent variable modeling with causal mediator models when unobserved confounding is present in the data. We provide evidence that the new g-estimation based estimator is robust to confounding and highlight finite sample size properties. We posted a preprint on arxiv.org

Morelli, S., Faleh, H. & Brandt, H (submitted). RAPSEM: Identifying Latent Mediators Without Sequential Ignorability via a Rank-Preserving Structural Equation Model. Article Github

New Publication: Case-to-Condition Ratios in Qualitative Comparative Analysis: Adding Cases Instead of Removing Conditions. Glaesser, J.

Tue, 02 Apr 2024 14:32:51 +0200

In qualitative comparative analysis, as with all methods, there is a question about how many cases are needed to make an analysis robust. In deciding on the number of cases, a key consideration is the number of conditions to be analyzed. I suggest that adding cases is preferable to dropping conditions if there are too many conditions relative to the number of cases. I first consider the relationship of low n and limited diversity, followed by an exploration of two scenarios: (1) cases in the study are the universe; (2) more cases could exist. I suggest that a simple rule or benchmark on how many cases to include in relation to the number of conditions is unlikely to be helpful since this depends at least in part on the goals and circumstances of the research. Finally, this issue is not confined to QCA but affects all types of research.

Glaesser, J. (2024). Case-to-Condition Ratios in Qualitative Comparative Analysis: Adding Cases Instead of Removing Conditions. Field Methods, https://doi.org/10.1177/1525822X241231479